Context: The workload at seaport container terminal is increasing gradually. We need to improve the performance of terminal to fulfill the demand. The key section of the container terminal is container stacking yard which is an integral part of the seaside and the landside. So its performance has the effects on both sides. The main problem in this area is unproductive moves of containers. However, we need a well-planned stacking area in order to increase the performance of terminal and maximum utilization of existing resources.

Objectives: In this work, we have analyzed the existing container stacking system at Helsingborg seaport container terminal, Sweden, investigated the already provided solutions of the problem and find the best optimization technique to get the best possible solution. After this, suggest the solution, test the proposed solution and analyzed the simulation based results with respect to the desired solution.

Methods: To identify the problem, methods and proposed solutions of the given problem in the domain of container stacking yard management, a literature review has been conducted by using some e-resources/databases. A GA with best parametric values is used to get the best optimize solution. A discrete event simulation model for container stacking in the yard has been build and integrated with genetic algorithm. A proposed mathematical model to show the dependency of cost minimization on the number of containers’ moves.

Results: The GA has been achieved the high fitness value versus generations for 150 containers to storage at best location in a block with 3 tier levels and to minimize the unproductive moves in the yard. A comparison between Genetic Algorithm and Tabu Search has been made to verify that the GA has performed better than other algorithm or not. A simulation model with GA has been used to get the simulation based results and to show the container handling by using resources like AGVs, yard crane and delivery trucks and container stacking and retrieval system in the yard. The container stacking cost is directly proportional to the number of moves has been shown by the mathematical model.

Conclusions: We have identified the key factor (unproductive moves) that is the base of other key factors (time & cost) and has an effect on the performance of the stacking yard and overall the whole seaport terminal. We have focused on this drawback of stacking system and proposed a solution that makes this system more efficient. Through this, we can save time and cost both. A Genetic Algorithm is a best approach to solve the unproductive moves problem in container stacking system.

Transactional memory (TM), a new programming paradigm, is one of the latest approaches to write programs for next generation multicore and multiprocessor systems. TM is an alternative to lock-based programming. It is a promising solution to a hefty and mounting problem that programmers are facing in developing programs for Chip Multi-Processor (CMP) architectures by simplifying synchronization to shared data structures in a way that is scalable and compos-able. Software Transactional Memory (STM) a full software approach of TM systems can be defined as non-blocking synchronization mechanism where sequential objects are automatically converted into concurrent objects. In this thesis, we present performance comparison of four different STM implementations – RSTM of V. J. Marathe, et al., TL2 of D. Dice, et al., TinySTM of P. Felber, et al. and SwissTM of A. Dragojevic, et al. It helps us in deep understanding of potential tradeoffs involved. It further helps us in assessing, what are the design choices and configuration parameters that may provide better ways to build better and efficient STMs. In particular, suitability of an STM is analyzed against another STM. A literature study is carried out to sort out STM implementations for experimentation. An experiment is performed to measure performance tradeoffs between these STM implementations. The empirical evaluations done as part of this thesis conclude that SwissTM has significantly higher throughput than state-of-the-art STM implementations, namely RSTM, TL2, and TinySTM, as it outperforms consistently well while measuring execution time and aborts per commit parameters on STAMP benchmarks. The results taken in transaction retry rate measurements show that the performance of TL2 is better than RSTM, TinySTM and SwissTM.

Cassandra is a NoSQL(Not only Structured Query Language) database which serves large amount of data with high availability .Cassandra data storage dimensioning also known as Cassandra capacity planning refers to predicting the amount of disk storage required when a particular product is deployed using Cassandra. This is an important phase in any product development lifecycle involving Cassandra data storage system. The capacity planning is based on many factors which are classified as Cassandra specific and Product specific.This study is to identify the different Cassandra specific and product specific factors affecting the disk space in Cassandra data storage system. Based on these factors a model is to be built which would predict the disk storage for Ericsson’s voucher system.A case-study is conducted on Ericsson’s voucher system and its Cassandra cluster. Interviews were conducted on different Cassandra users within Ericsson R&D to know their opinion on capacity planning approaches and factors affecting disk space for Cassandra. Responses from the interviews were transcribed and analyzed using grounded theory.A total of 9 Cassandra specific factors and 3 product specific factors are identified and documented. Using these 12 factors a model was built. This model was used in predicting the disk space required for voucher system’s Cassandra.The factors affecting disk space for deploying Cassandra are now exhaustively identified. This makes the capacity planning process more efficient. Using these factors the Voucher system’s disk space for deployment is predicted successfully.

Context. Building autonomous racing car controllers is a growing field of computer science which has been receiving great attention lately. An approach named Artificial Potential Fields (APF) is used widely as a path finding and obstacle avoidance approach in robotics and vehicle motion controlling systems. The use of APF results in a collision free path, it can also be used to achieve other goals such as overtaking and maneuverability. Objectives. The aim of this thesis is to build an autonomous racing car controller that can achieve good performance in terms of speed, time, and damage level. To fulfill our aim we need to achieve optimality in the controller choices because racing requires the highest possible performance. Also, we need to build the controller using algorithms that does not result in high computational overhead. Methods. We used Particle Swarm Optimization (PSO) in combination with APF to achieve optimal car controlling. The Open Racing Car Simulator (TORCS) was used as a testbed for the proposed controller, we have conducted two experiments with different configuration each time to test the performance of our APF- PSO controller. Results. The obtained results showed that using the APF-PSO controller resulted in good performance compared to top performing controllers. Also, the results showed that the use of PSO proved to enhance the performance compared to using APF only. High performance has been proven in the solo driving and in racing competitions, with the exception of an increased level of damage, however, the level of damage was not very high and did not result in a controller shut down. Conclusions. Based on the obtained results we have concluded that the use of PSO with APF results in high performance while taking low computational cost.

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

This thesis investigated the potential open data as a complementary resource for Anomaly Detection (AD) in the Maritime Surveillance (MS) domain. A framework for AD was proposed based on the usage of open data sources along with other traditional sources of data. According to the proposed AD framework and the algorithms for implementing the expert rules, the Open Data Anomaly Detection System (ODADS) was developed. To evaluate the accuracy of the system, an experiment on the vessel traffic data was conducted and an accuracy of 99% was obtained for the system. There was a false negative case in the system results that decreased the accuracy. It was due to incorrect AIS data in a special situation that was not possible to be handled by the detection rules in the scope of this thesis. The validity of the results was investigated by the subject matter experts from the Swedish Coastguard. The validation results showed that the majority of the ODADS evaluated anomalies were true alarms. Moreover, a potential information gap in the closed data sources was observed during the validation process. Despite the high number of true alarms, the number of false alarms was also considerable that was mainly because of the inaccurate open data. This thesis provided insights into the open data as a complement to the common data sources in the MS domain and is concluded that using open data will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.

Which security holes and security methods do IEEE 802.11b and Bluetooth offer? Which standard provides best security methods for companies? These are two interesting questions that this thesis will be about. The purpose is to give companies more information of the security aspects that come with using WLANs. An introduction to the subject of WLAN is presented in order to give an overview before the description of the two WLAN standards; IEEE 802.11b and Bluetooth. The thesis will give an overview of how IEEE 802.11b and Bluetooth works, a in depth description about the security issues of the two standards will be presented, security methods available for companies, the security flaws and what can be done in order to create a secure WLAN are all important aspects to this thesis. In order to give a guidance of which WLAN standard to choose, a comparison of the two standards with the security issues in mind, from a company's point of view is described. We will present our conclusion which entails a recommendation to companies to use Bluetooth over IEEE 802.11b, since it offers better security methods.

IPTV (Internet Protocol Television), a new and modern concept of emerging technologies with focus on providing cutting edge high-resolution television, broadcast, and other fascinating services, is now easily available with only requirement of high-speed internet. Everytime a new technology is made local, it faces tremendous problems whether from technological point of view to enhance the performance or when it comes down to satisfy the customers. This cutting edge technology has provided researchers to embark and play with different tools to provide better quality while focusing on existing tools. Our target in dissertation is to provide a few interesting facets of IPTV and come up with a concept of introducing an imaginary cache that can re-collect the packets travelling from streaming server to the end user. In the access node this cache would be fixed and then on the basis of certain pre-assumed research work we can conclude how quick retransmission can take place when the end user responds back using RTCP protocol and asks for the retransmission of corrupted/lost packets. In the last section, we plot our scenario of streaming server on one side and client, end user on the other end and make assumption on the basis of throughput, response time and traffic.

Business Intelligence can bring critical capabilities to an organization, but the implementation of such capabilities is often plagued with problems and issues. Why is it that certain projects fail, while others succeed? The theoretical problem and the aim of this thesis is to identify the factors that are present in successful Business Intelligence projects and organize them into a framework of critical success factors. A survey was conducted during the spring of 2011 to collect primary data on Business Intelligence projects. It was directed to a number of different professionals operating in the Business Intelligence field in large enterprises, primarily located in Poland and primarily vendors, but given the similarity of Business Intelligence initiatives across countries and increasing globalization of large enterprises, the conclusions from this thesis may well have relevance and be applicable for projects conducted in other countries. Findings confirm that Business Intelligence projects are wrestling with both technological and nontechnological problems, but the non-technological problems are found to be harder to solve as well as more time consuming than their technological counterparts. The thesis also shows that critical success factors for Business Intelligence projects are different from success factors for IS projects in general and Business Intelligences projects have critical success factors that are unique to the subject matter. Major differences can be predominately found in the non-technological factors, such as the presence of a specific business need to be addressed by the project and a clear vision to guide the project. Results show that successful projects have specific factors present more frequently than nonsuccessful. Such factors with great differences are the type of project funding, business value provided by each iteration of the project and the alignment of the project to a strategic vision for Business Intelligence. Furthermore, the thesis provides a framework of critical success factors that, according to the results of the study, explains 61% of variability of success of projects. Given these findings, managers responsible for introducing Business Intelligence capabilities should focus on a number of non-technological factors to increase the likelihood of project success. Areas which should be given special attention are: making sure that the Business Intelligence solution is built with end users in mind, that the Business Intelligence solution is closely tied to company‟s strategic vision and that the project is properly scoped and prioritized to concentrate on best opportunities first. Keywords: Critical Success Factors, Business Intelligence, Enterprise Data Warehouse Projects, Success Factors Framework, Risk Management

In this thesis, the impact of packet losses on the quality of received videos sent across a network that exhibit normal network perturbations such as jitters, delays, packet drops etc has been examined. Dynamic behavior of a normal network has been simulated using Linux and the Network Emulator (NetEm). Peoples’ perceptions on the quality of the received video were used in rating the qualities of several videos with differing speeds. In accordance with ITU’s guideline of using Mean Opinion Scores (MOS), the effects of packet drops were analyzed. Excel and Matlab were used as tools in analyzing the peoples’ opinions which indicates the impacts that different loss rates has on the transmitted videos. Statistical methods used for evaluation of data are mean and variance. We conclude that people have convergence of opinions when losses become extremely high on videos with highly variable scene changes

The purpose of this study is to present guidelines that can be followed when introducing Service-oriented architecture through the use of Web services. This guideline will be especially useful for organizations migrating from their existing legacy systems where the need also arises to consider the financial implications of such an investment whether it is worthwhile or not. The proposed implementation guide aims at increasing the chances of IT departments in organizations to ensure a successful integration of SOA into their system and secure strong financial commitment from the executive management. Service oriented architecture technology is a new concept, a new way of looking at a system which has emerged in the IT world and can be implemented by several methods of which Web services is one platform. Since it is a developing technology, organizations need to be cautious on how to implement this technology to obtain maximum benefits. Though a well-designed, service-oriented environment can simplify and streamline many aspects of information technology and business, achieving this state is not an easy task. Traditionally, management finds it very difficult to justify the considerable cost of modernization, let alone shouldering the risk without achieving some benefits in terms of business value. The study identifies some common best practices of implementing SOA and the use of Web services, steps to successfully migrate from legacy systems to componentized or service enabled systems. The study also identified how to present financial return on investment and business benefits to the management in order to secure the necessary funds. This master thesis is based on academic literature study, professional research journals and publications, interview with business organizations currently working on service oriented architecture. I present guidelines that can be of assistance to migrate from legacy systems to service-oriented architecture based on the analysis from comparing information sources mentioned above.

The goal of this master’s thesis is to identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analyzed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5.

Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.

Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions that affects software quality, e.g., how to allocate testing resources, develop testing schedules and to decide when to stop testing, needs to be as stable and accurate as possible. The objective of this thesis is to investigate how search-based techniques can support decision-making and help control variation in software V&V activities, thereby indirectly improving software quality. Several themes in providing this support are investigated: predicting reliability of future software versions based on fault history; fault prediction to improve test phase efficiency; assignment of resources to fixing faults; and distinguishing fault-prone software modules from non-faulty ones. A common element in these investigations is the use of search-based techniques, often also called metaheuristic techniques, for supporting the V&V decision-making processes. Search-based techniques are promising since, as many problems in real world, software V&V can be formulated as optimization problems where near optimal solutions are often good enough. Moreover, these techniques are general optimization solutions that can potentially be applied across a larger variety of decision-making situations than other existing alternatives. Apart from presenting the current state of the art, in the form of a systematic literature review, and doing comparative evaluations of a variety of metaheuristic techniques on large-scale projects (both industrial and open-source), this thesis also presents methodological investigations using search-based techniques that are relevant to the task of software quality measurement and prediction. The results of applying search-based techniques in large-scale projects, while investigating a variety of research themes, show that they consistently give competitive results in comparison with existing techniques. Based on the research findings, we conclude that search-based techniques are viable techniques to use in supporting the decision-making processes within software V&V activities. The accuracy and consistency of these techniques make them important tools when developing future decision-support for effective management of software V&V activities.

The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. We applied eight classification techniques to the task of identifying fault-prone modules, representing a variety of approaches, including a standard statistical technique for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Na\"{i}ve Bayes), machine-learning techniques (support vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. \emph{Results:} Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, GP showed impressive results in comparison with other techniques for predicting fault-prone modules at both integration and system test levels. The use of faults-slip-through metric in general provided good prediction results at the two test levels. The accuracy of GP is statistically significant in comparison with majority of the techniques for predicting fault-prone modules at integration and system test levels. (ii) Faults-slip-through metric has the potential to be a generally useful predictor of fault-proneness at integration and system test levels.

Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.

Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing the results with traditional approaches to compare efficiency gains.

Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

Requirements prioritization is an important part of developing the right product in the right time. There are different ideas about which method is the best to use when prioritizing requirements. This thesis takes a closer look at five different methods and then put them into an controlled experiment, in order to find out which of the methods that would be the best method to use. The experiment was designed to find out which method yields the most accurate result, the method’s ability to scale up to many more requirements, what time it took to prioritize with the method, and finally how easy the method was to use. These four criteria combined will indicate which method is more suitable, i.e. be the best method, to use in prioritizing of requirements. The chosen methods are the well-known analytic hierarchy process, the computer algorithm binary search tree, and from the ideas of extreme programming come planning game. The fourth method is an old but well used method, the 100 points method. The last method is a new method, which combines planning game with the analytic hierarchy process. Analysis of the data from the experiment indicates that the planning game combined with analytic hierarchy process could be a good candidate. However, the result from the experiment clearly indicates that the binary search tree yields accurate result, is able to scale up and was the easiest method to use. For these three reasons the binary search tree clearly is the better method to use for prioritizing requirements

In this research the authors explore a hybrid approach which usesthe basic concept of key frame animation together with proceduralanimation to reduce the number of key frames needed for an animationclip. The two approaches are compared by conducting anexperiment where the participating subjects were asked to ratethem based on their visual appeal.

Context. Industry is moving from co-located form of development to a distributed development in order to achieve different benefits such as cost reduction, access to skillful labor and around the clock working etc. This transfer requires industry to face different challenges such as communication, coordination and monitoring problems. Risk of project failure can be increased, if industry does not address these problems. This thesis is about providing the solutions of these problems in term of effective roles and responsibilities that may have positive impact on GSD team. Objectives. In this study we have developed framework for suggesting roles and responsibilities for GSD team. This framework consists of problems and casual dependencies between them which are related to team’s ineffectiveness, then suggestions in terms of roles and responsibilities have been presented in order to have an effective team in GSD. This framework, further, has been validated in industry through a survey that determines which are the effective roles and responsibilities in GSD. Methods. We have two research methods in this study 1) systematic literature review and 2) survey. Complete protocol for planning, conducting and reporting the review as well as survey has been described in their respective sections in this thesis. A systematic review is used to develop the framework whereas survey is used for framework validation. We have done static validation of framework. Results. Through SLR, we have identified 30 problems, 33 chains of problems. We have identified 4 different roles and 40 different responsibilities to address these chains of problems. During the validation of the framework, we have validated the links between suggested roles and responsibilities and chains of problems. Addition to this, through survey, we have identified 20 suggestions that represents strong positive impact on chains of problems in GSD in relation to team’s effectiveness. Conclusions. We conclude that implementation of effective roles and responsibilities in GSD team to avoid different problems require considerable attention from researchers and practitioners which can guarantee team’s effectiveness. Implementation of proper roles and responsibilities has been mentioned as one of the successful strategies for increasing team’s effectiveness in the literature, but which particular roles and responsibilities should be implemented still need to be addressed. We also conclude that there must be basic responsibilities associated with any particular role. Moreover, we conclude that there is a need for further development and empirical validation of different frameworks for suggesting roles and responsibilities in full scale industry trials.

The use of internet has increased over the past years. Many users may not have good intentions. Some people use the internet to gain access to the unauthorized information. Although absolute security of information is not possible for any network connected to the Internet however, firewalls make an important contribution to the network security. A firewall is a barrier placed between the network and the outside world to prevent the unwanted and potentially damaging intrusion of the network. This thesis compares the performance of Linux packet filtering firewalls, i.e. iptables and shorewall. The firewall performance testing helps in selecting the right firewall as needed. In addition, it highlights the strength and weakness of each firewall. Both firewalls were tested by using the identical parameters. During the experiments, recommended benchmarking methodology for firewall performance testing is taken into account as described in RFC 3511. The comparison process includes experiments which are performed by using different tools. To validate the effectiveness of firewalls, several performance metrics such as throughput, latency, connection establishment and teardown rate, HTTP transfer rate and system resource consumption are used. The experimental results indicate that the performance of Iptables firewall decreases as compared to shorewall in all the aspects taken into account. All the selected metrics show that large numbers of filtering rules have a negative impact on the performance of both firewalls. However, UDP throughput is not affected by the number of filtering rules. The experimental results also indicate that traffic sent with different packet sizes do not affect the performance of firewalls.

Paralleling the rapid advancement in the network evolution is the need for advanced network traffic management surveillance. The increasing number and variety of services being offered by communication networks has fuelled the demand for optimized load management strategies. The problem of Load Control Management in Intelligent Networks has been studied previously and four Multi-Agent architectures have been proposed. The objective of this thesis is to investigate one of the quality attributes namely, scalability of the four Multi-Agent architectures. The focus of this research would be to resize the network and study the performance of the different architectures in terms of Load Control Management through different scalability attributes. The analysis has been based on experimentation through simulations. It has been revealed through the results that different architectures exhibit different performance behaviors for various scalability attributes at different network sizes. It has been observed that there exists a trade-off in different scalability attributes as the network grows. The factors affecting the network performance at different network settings have been observed. Based on the results from this study it would be easier to design similar networks for optimal performance by controlling the influencing factors and considering the trade-offs involved.

Abstract Context: Online social networks such as Facebook, Twitter, and MySpace have become the preferred interaction, entertainment and socializing facility on the Internet. However, these social network services also bring privacy issues in more limelight than ever. Several privacy leakage problems are highlighted in the literature with a variety of suggested countermeasures. Most of these measures further add complexity and management overhead for the user. One ignored aspect with the architecture of online social networks is that they do not offer any mechanism to calculate the strength of relationship between individuals. This information is quite useful to identify possible privacy threats. Objectives: In this study, we identify users’ privacy concerns and their satisfaction regarding privacy control measures provided by online social networks. Furthermore, this study explores data mining techniques to predict the levels/intensity of friendship in online social networks. This study also proposes a technique to utilize predicted friendship levels for privacy preservation in a semi-automatic privacy framework. Methods: An online survey is conducted to analyze Facebook users’ concerns as well as their interaction behavior with their good friends. On the basis of survey results, an experiment is performed to justify practical demonstration of data mining phases. Results: We found that users are concerned to save their private data. As a precautionary measure, they restrain to show their private information on Facebook due to privacy leakage fears. Additionally, individuals also perform some actions which they also feel as privacy vulnerability. This study further identifies that the importance of interaction type varies while communication. This research also discovered, “mutual friends” and “profile visits”, the two non-interaction based estimation metrics. Finally, this study also found an excellent performance of J48 and Naïve Bayes algorithms to classify friendship levels. Conclusions: The users are not satisfied with the privacy measures provided by the online social networks. We establish that the online social networks should offer a privacy mechanism which does not require a lot of privacy control effort from the users. This study also concludes that factors such as current status, interaction type need to be considered with the interaction count method in order to improve its performance. Furthermore, data mining classification algorithms are tailor-made for the prediction of friendship levels.

The role of information systems has become very important in today’s world. It is not only the business organizations who use information systems but the governments also posses’ very critical information systems. The need is to make information systems available at all times under any situation. Information systems must have the capabilities to resist against the dangers to its services,performance & existence, and recover to its normal working state with the available resources in catastrophic situations. The information systems with such a capability can be called resilient information systems. This thesis is written to define resilient information systems, suggest its meta-model and to explain how existing technologies can be utilized for the development of resilient information system.

This project presents the description, design and the implementation of a 4-channel microphone array that is an adaptive sub-band generalized side lobe canceller (GSC) beam former uses for video conferencing, hands-free telephony etc, in a noisy environment for speech enhancement as well as noise suppression. The side lobe canceller evaluated with both Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) adaptation. A testing structure is presented; which involves a linear 4-microphone array connected to collect the data. Tests were done using one target signal source and one noise source. In each microphone’s, data were collected via fractional time delay filtering then it is divided into sub-bands and applied GSC to each of the subsequent sub-bands. The overall Signal to Noise Ratio (SNR) improvement is determined from the main signal and noise input and output powers, with signal-only and noise-only as the input to the GSC. The NLMS algorithm signiﬁcantly improves the speech quality with noise suppression levels up to 13 dB while LMS algorithm is giving up to 10 dB. All of the processing for this thesis is implemented on a computer using MATLAB and validated by considering different SNR measure under various types of blocking matrix, different step sizes, different noise locations and variable SNR with noise.

Context: A District Heating System (DHS) uses a central heating plant to produce and distribute hot water in a community. Such a plant is connected with consumers’ premises to provide them with hot water and space heating facilities. Variations in the consumption of heat energy depend upon different factors like difference in energy prices, living standards, environmental effects and economical conditions etc. These factors can manage intelligently by advanced tools of Information and Communication Technology (ICT) such as smart metering. That is a new and emerging technology; used normally for metering of District Heating (DH), district cooling, electricity and gas. Traditional meters measures overall consumption of energy, in contrast smart meters have the ability to frequently record and transmit energy consumption statistics to both energy providers and consumers by using their communication networks and network management systems. Objectives: First objective of conducted study was providing energy consumption/saving suggestions on smart metering display for accepted consumer behavior, proposed by the energy providers. Our second objective was analysis of financial benefits for the energy provides, which could be expected through better consumer behavior. Third objective was analysis of energy consumption behavior of the residential consumes that how we can support it. Moreover, forth objective of the study was to use extracted suggestions of consumer behaviors to propose Extended Smart Metering Display for improving energy economy. Methods: In this study a background study was conducted to develop basic understanding about District Heat Energy (DHE), smart meters and their existing display, consumer behaviors and its effects on energy consumption. Moreover, interviews were conducted with representatives of smart heat meters’ manufacturer, energy providers and residential consumers. Interviews’ findings enabled us to propose an Extended Smart Metering Display, that satisfies recommendations received from all the interviewees and background study. Further in this study, a workshop was conducted for the evaluation of the proposed Extended Smart Metering Display which involved representatives of smart heat meters’ manufacture and residential energy consumers. DHE providers also contributed in this workshop through their comments in online conversation, for which an evaluation request was sent to member companies of Swedish District Heating Association. Results: Informants in this research have different levels of experiences. Through a systematic procedure we have obtained and analyzed findings from all the informants. To fulfill the energy demands during peak hours, the informants emphasized on providing efficient energy consumption behavior to be displayed on smart heat meters. According to the informants, efficient energy consumption behavior can be presented through energy consumption/saving suggestions on display of smart meters. These suggestions are related to daily life activities like taking bath and shower, cleaning, washing and heating usage. We analyzed that efficient energy consumption behavior recommended by the energy providers can provide financial improvements both for the energy providers and the residential consumers. On the basis of these findings, we proposed Extended Smart Metering Display to present information in simple and interactive way. Furthermore, the proposed Extended Smart Metering Display can also be helpful in measuring consumers’ energy consumption behavior effectively. Conclusions: After obtaining answers of the research questions, we concluded that extension of existing smart heat meters’ display can effectively help the energy providers and the residential consumers to utilize the resources efficiently. That is, it will not only reduce energy bills for the residential consumers, but it will also help the energy provider to save scarce energy and enable them to serve the consumers better in peak hours. After deployment of the proposed Extended Smart Metering Display the energy providers will able to support the consumers’ behavior in a reliable way and the consumers will find/follow the energy consumption/saving guidelines easily.

Context. Reminder system offers flexibility in daily life activities and assists to be independent. The reminder system not only helps reminding daily life activities, but also serves to a great extent for the people who deal with health care issues. For example, a health supervisor who monitors people with different health related problems like people with disabilities or mild dementia. Traditional reminders which are based on a set of defined activities are not enough to address the necessity in a wider context. To make the reminder more flexible, the user’s current activities or contexts are needed to be considered. To recognize user’s current activity, different types of sensors can be used. These sensors are available in Smartphone which can assist in building a more contextual reminder system. Objectives. To make a reminder context based, it is important to identify the context and also user’s activities are needed to be recognized in a particular moment. Keeping this notion in mind, this research aims to understand the relevant context and activities, identify an effective way to recognize user’s three different activities (drinking, walking and jogging) using Smartphone sensors (accelerometer and gyroscope) and propose a model to use the properties of the identification of the activity recognition. Methods. This research combined a survey and interview with an exploratory Smartphone sensor experiment to recognize user’s activity. An online survey was conducted with 29 participants and interviews were held in cooperation with the Karlskrona Municipality. Four elderly people participated in the interview. For the experiment, three different user activity data were collected using Smartphone sensors and analyzed to identify the pattern for different activities. Moreover, a model is proposed to exploit the properties of the activity pattern. The performance of the proposed model was evaluated using machine learning tool, WEKA. Results. Survey and interviews helped to understand the important activities of daily living which can be considered to design the reminder system, how and when it should be used. For instance, most of the participants in the survey are used to using some sort of reminder system, most of them use a Smartphone, and one of the most important tasks they forget is to take their medicine. These findings helped in experiment. However, from the experiment, different patterns have been observed for three different activities. For walking and jogging, the pattern is discrete. On the other hand, for drinking activity, the pattern is complex and sometimes can overlap with other activities or can get noisy. Conclusions. Survey, interviews and the background study provided a set of evidences fostering reminder system based on users’ activity is essential in daily life. A large number of Smartphone users promoted this research to select a Smartphone based on sensors to identify users’ activity which aims to develop an activity based reminder system. The study was to identify the data pattern by applying some simple mathematical calculations in recorded Smartphone sensors (accelerometer and gyroscope) data. The approach evaluated with 99% accuracy in the experimental data. However, the study concluded by proposing a model to use the properties of the identification of the activities and developing a prototype of a reminder system. This study performed preliminary tests on the model, but there is a need for further empirical validation and verification of the model.

This thesis paper proposes a Medium Access Control (MAC) protocol for wireless networks, termed as CD-MMAC that utilizes multiple channels and incorporates opportunistic cooperative diversity dynamically to improve its performance. The IEEE 802.11b standard protocol allows the use of multiple channels available at the physical layer but its MAC protocol is designed only for a single channel. The proposed protocol utilizes multiple channels by using single interface and incorporates opportunistic cooperative diversity by using cross-layer MAC. The new protocol leverages the multi-rate capability of IEEE 802.11b and allows wireless nodes far away from destination node to transmit at a higher rate by using intermediate nodes as a relays. The protocol improves network throughput and packet delivery ratio significantly and reduces packet delay. The performance improvement is further evaluated by simulation and analysis.

Context. Software testing is one of the crucial phases in software development life cycle (SDLC). Among the different manual testing methods in software testing, Exploratory testing (ET) uses no predefined test cases to detect defects. Objectives. The main objective of this study is to test the effectiveness of ET in detecting defects at different software test levels. The objective is achieved by formulating hypotheses, which are later tested for acceptance or rejection. Methods. Methods used in this thesis are literature review and experiment. Literature review is conducted to get in-depth knowledge on the topic of ET and to collect data relevant to ET. Experiment was performed to test hypotheses specific to the three different testing levels : unit , integration and system. Results. The experimental results showed that using ET did not find all the seeded defects at the three levels of unit, integration and system testing. The results were analyzed using statistical tests and interpreted with the help of bar graphs. Conclusions. We conclude that more research is required in generalizing the benefits of ET at different test levels. Particularly, a qualitative study to highlight factors responsible for the success and failure of ET is desirable. Also we encourage a replication of this experiment with subjects having a sound technical and domain knowledge.

Using eyes as an input modality for different control environments is a great area of interest for enhancing the bandwidth of human machine interaction and providing interaction functions when the use of hands is not possible. Interface design requirements in such implementations are quite different from conventional application areas. Both command-execution and feedback observation tasks may be performed by human eyes simultaneously. In order to control the motion of a mobile robot by operator gaze interaction, gaze contingent regions in the operator interface are used to execute robot movement commands, with different screen areas controlling specific directions. Dwell time is one of the most established techniques to perform an eye-click analogous to a mouse click. But repeated dwell time while switching between gaze-contingent regions and feedback-regions decreases the performance of the application. We have developed a dynamic gaze-contingent interface in which we merge gaze-contingent regions with feedback-regions dynamically. This technique has two advantages: Firstly it improves the overall performance of the system by eliminating repeated dwell time. Secondly it reduces fatigue of the operator by providing a bigger area to fixate in. The operator can monitor feedback with more ease while sending commands at the same time.

Today’s cell phones include more and more functionality, it is no longer uncommon to be able to connect to the Internet or take photos with the cell phone. In this thesis the question is if the cell phone users are interested in this new functionality. Are there people who are interested in a cell phone that in this thesis is called “Basmobilen” (the entry level phone), which include the following functions; phone call, SMS, phone book, calling lists, clock and the possibility to change the call signal. After some historical insight in the development of the cell phone there is a description of the entry level phone (basmobilen). The question about what the cell phone users are interested in is answered through a poll, interviews and diaries. The result shows that the entry level phone (basmobilen) is interesting for the persons who answered the poll, but there are some of them who want some additional functionality. This is discussed further in the discussion part of the thesis, where also a suggestion on how cell phones should be built in the future, to fit as large user groups as possible, is made.

Annually, road accidents cause more than 1.2 million deaths, 50 million injuries, and US$ 518 billion of economic cost globally. About 90% of the accidents occur due to human errors such as bad awareness, distraction, drowsiness, low training, fatigue etc. These human errors can be minimized by using advanced driver assistance system (ADAS) which actively monitors the driving environment and alerts a driver to the forthcoming danger, for example adaptive cruise control, blind spot detection, parking assistance, forward collision warning, lane departure warning, driver drowsiness detection, and traffic sign recognition etc. Unfortunately, these systems are provided only with modern luxury cars because they are very expensive due to numerous sensors employed. Therefore, camera-based ADAS are being seen as an alternative because a camera has much lower cost, higher availability, can be used for multiple applications and ability to integrate with other systems. Aiming at developing a camera-based ADAS, we have performed an ethnographic study of drivers in order to find what information about the surroundings could be helpful for drivers to avoid accidents. Our study shows that information on speed, distance, relative position, direction, and size & type of the nearby vehicles & other objects would be useful for drivers, and sufficient for implementing most of the ADAS functions. After considering available technologies such as radar, sonar, lidar, GPS, and video-based analysis, we conclude that video-based analysis is the fittest technology that provides all the essential support required for implementing ADAS functions at very low cost. Finally, we have proposed a Smart-Dashboard system that puts technologies – such as camera, digital image processor, and thin display – into a smart system to offer all advanced driver assistance functions. A basic prototype, demonstrating three functions only, is implemented in order to show that a full-fledged camera-based ADAS can be implemented using MATLAB.

Road accidents cause a great loss to human lives and assets. Most of the accidents occur due to human errors, such as bad awareness, distraction, drowsiness, low training, and fatigue. Advanced driver assistance system (ADAS) can reduce the human errors by keeping an eye on the driving environment and warning a driver to the upcoming danger. However, these systems come only with modern luxury cars because of their high cost and complexity due to several sensors employed. Therefore, camera-based ADAS are becoming an option due to their lower cost, higher availability, numerous applications and ability to combine with other systems. Targeting at designing a camera-based ADAS, we have conducted an ethnographic study of drivers to know what information about the driving environment would be useful in preventing accidents. It turned out that information on speed, distance, relative position, direction, and size and type of the nearby objects would be useful and enough for implementing most of the ADAS functions. Several camera-based techniques are available for capturing the required information. We propose a novel design of an integrated camera-based ADAS that puts technologies-such as five ordinary CMOS image sensors, a digital image processor, and a thin display-into a smart system to offer a dozen advanced driver assistance functions. A basic prototype is also implemented using MATLAB. Our design and the prototype testify that all the required technologies are now available for implementing a full-fledged camera-based ADAS.

The need of change is essential for a software system to reside longer in the market. Change implementation is only done through the maintenance and successful software maintenance gives birth to a new software release that is a refined form of the previous one. This phenomenon is known as the evolution of the software. To transfer software from lower to upper or better form, maintainers have to get familiar with the particular aspects of software i.e. source code and documentation. Due to the poor quality of documentation maintainers often have to rely on source code. So, thorough understanding of source code is necessary for effective change implementation. This study explores the code comprehension problems discussed in the literature and prioritizes them according to their severity level given by maintenance personnel in the industry. Along with prioritizing the problems, study also presents the maintenance personnel suggested methodologies for improving code comprehension. Consideration of these suggestions in development might help in shortening the maintenance and evolution time.

In this thesis, the concept of virtual reality has been elaborated in the context of games, industrial design and manufacturing. The main purpose of this master’s thesis is to create a virtual environment for games that are near to the reality and according to the human nature through aspects like better interface, simulation, lights, shadow effects and their types. The importance of these aspects regarding realistic virtual environment is complemented through the comparison between two environments i.e. desktop and CAVE on a flight simulation program.

In this thesis, I examined various signalling both in wired and mobile networks, with more emphasis on SIGTRAN. The SIGTRAN is the protocol suite applicable in the current new generation and next-generation networks, most especially as it enables service provider to be able to interpolate both wireline and wireless services within the same architecture. This concept is an important component in today’s Triple-play communication, and hence this thesis has provided a broad view on Signalling and Protocol Gateways in Traditional and Next Generations Networks. Signal flow in a typical new generation network was examined by carrying out discrete event simulation of UMTS network using OPNET modeller 14.5. Through both Packet-Switching (PS) and Circuit-Switching (CS) signalling, I was able to examine the QoS on a UMTS. Precisely, I looked at throughput on UMTS network by implementing WFQ and MDRR scheduling schemes.

Intelligent multi-agent systems offer promising approaches for knowledge-intensive distributed applications. Now that such systems are becoming applied on a wider industrial scale, there is a practical need for structured analysis and design methods, similarly as exist for more conventional information and knowledge systems. This is still lacking for intelligent agent software. In this paper, we describe how the process of agent communication specification can be carried out through a structured analysis approach. The structured analysis approach we propose is an integrated extension of the CommonKADS methodology, a widely used standard for knowledge analysis and systems development. Our approach is based on and illustrated by a large-scale multi-agent application for distributed energy load management in industries and households, called Homebots, which is discussed as an extensive industrial case study.

The process of agent communication modeling has not yet received much attention in the knowledge systems area. Conventional knowledge systems are rather simple with respect to their communication structure: often it is a straightforward question-and-answer sequence between system and end user. However, this is different in recent intelligent multi-agent systems. Therefore, agent communication aspects are now in need of a much more advanced treatment in knowledge management, acquisition and modeling. In general, a much better integration between the respective achievements of multi-agent and knowledge-based systems modeling is an important research goal. In this paper, we describe how agent communications can be specified as an extension of well-known knowledge modeling techniques. The emphasis is on showing how a structured process of communication requirements analysis proceeds, based on existing results from agent communication languages. The guidelines proposed are illustrated by and based on a large-scale industrial multi-agent application for distributed energy load management in industries and households, called Homebots. Homebots enable cost savings in energy consumption by coordinating their actions through an auction mechanism.